An open source tool named SimpleTree, capable of modelling highly accurate cylindrical tree models from terrestrial laser scan point clouds, is presented and evaluated. All important functionalities, accessible in the software via buttons and dialogues, are described including the explanation of all necessary input parameters. The method is validated utilizing 101 point clouds of six different tree species, in the main evergreen and coniferous trees. All scanned trees have been destructively harvested to get accurate estimates of above ground biomass with which we assess the accuracy of the SimpleTree-reconstructed cylinder models. The trees were grouped into four data sets and for each one a Concordance Correlation Coefficient of at least 0. 92 (0.92, 0.97, 0.92, 0.94) and an total relative error at most ∼ 8 % (2.42%, 3.59%, -4.59%, 8.27%) was achieved in the comparison of the model results to the ground truth data. A global statistical improvement of derived cylinder radii is presented as well as an efficient optimization approach to Forests 2015, 6 4246 automatically improve user given input parameters. An additional check of the SimpleTree results is presented via comparison to the results of trees reconstructed using an alternative, published method.

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This paper presents a method for fitting cylinders into a point cloud, derived from a terrestrial laser-scanned tree. Utilizing high scan quality data as the input, the resulting models describe the branching structure of the tree, capable of detecting branches with a diameter smaller than a centimeter. The cylinders are stored as a hierarchical tree-like data structure encapsulating parent-child neighbor relations and incorporating the tree's direction of growth. This structure enables the efficient extraction of tree components, such as the stem or a single branch. The method was validated both by applying a comparison of the resulting cylinder models with ground truth data and by an analysis between the input point clouds and the models. Tree models were accomplished representing more than 99% of the input point cloud, with an average distance from the cylinder model to the point cloud within sub-millimeter accuracy. After validation, the method was applied to build two allometric models based on 24 tree point clouds as an example of the application. Computation terminated successfully within less than 30 min. For the model predicting the total above ground volume, the coefficient of determination was 0.965, showing the high potential of terrestrial laser-scanning for forest inventories.

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ABSTRACT. The choice between different forest management practices is a crucial step in short, medium, and long-term decision making in forestry and when setting up measures to support a regional or national forest policy. Some conditions such as biogeographically determined site factors, exposure to major disturbances, and societal demands are predetermined, whereas operational processes such as species selection, site preparation, planting, tending, or thinning can be altered by management. In principle, the concept of a forest management approach provides a framework for decision making, including a range of silvicultural operations that influence the development of a stand or group of trees over time. These operations vary among silvicultural systems and can be formulated as a set of basic principles. Consequently, forest management approaches are essentially defined by coherent sets of forest operation processes at a stand level.Five ideal forest management approaches (FMAs) representing a gradient of management intensity are described using specific sets of basic principles that enable comparison across European forests. Each approach is illustrated by a regional European case study. The observed regional variations resulting from changing species composition, stand density, age structure, stand edges, and site conditions can be interpreted using the FMA framework. Despite being arranged along an intensity gradient, the forest management approaches are not considered to be mutually exclusive, as the range of options allows for greater freedom in selecting potential silvicultural operations. As derived goods and services are clearly affected, the five forest management approaches have implications for sustainability. Thus, management objectives can influence the balance between the economic, ecological, and social dimensions of sustainability. The utility of this framework is further demonstrated through the different contributions to this special issue.

Two-photon microscopy is indispensable for deep tissue and intravital imaging. However, current technology based on single-beam point scanning has reached sensitivity and speed limits because higher performance requires higher laser power leading to sample degradation. We utilize a multifocal scanhead splitting a laser beam into a line of 64 foci, allowing sample illumination in real time at full laser power. This technology requires charge-coupled device field detection in contrast to conventional detection by photomultipliers. A comparison of the optical performance of both setups shows functional equivalence in every measurable parameter down to penetration depths of 200 microm, where most actual experiments are executed. The advantage of photomultiplier detection materializes at imaging depths >300 microm because of their better signal/noise ratio, whereas only charge-coupled devices allow real-time detection of rapid processes (here blood flow). We also find that the point-spread function of both devices strongly depends on tissue constitution and penetration depth. However, employment of a depth-corrected point-spread function allows three-dimensional deconvolution of deep-tissue data up to an image quality resembling surface detection.

This paper presents a method for predicting the above ground leafless biomass of trees in a non destructive way. We utilize terrestrial laserscan data to predict the volume of the trees. Combining volume estimates with density measurements leads to biomass predictions. Thirty-six trees of three different species are analyzed: evergreen coniferous Pinus massoniana, evergreen broadleaved Erythrophleum fordii and leafless deciduous Quercus petraea. All scans include a large number of noise points; denoising procedures are presented in detail. Density values are considered to be a minor source of error in the method if applied to stem segments, as comparison to ground truth data reveals that prediction errors for the tree volumes are in accordance with biomass prediction errors. While tree compartments with a diameter larger than 10 cm can be modeled accurately, smaller ones, especially twigs with a diameter smaller than 4 cm, are often largely overestimated. Better prediction results could be achieved by applying a biomass expansion factor to the biomass of compartments with a diameter larger than 10 cm. With this second method the average prediction error for Q. petraea could be reduced from 33.84% overestimation to 3.56%. E. fordii results could also be improved reducing the average prediction error from −17.24% to −7.30%. Only P. massoniana results had a low prediction error of 2.75% utilizing the total TLS-estimated volume, which was not improved by the biomass expansion method (3.82%).

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